AI Agents in Production: The Reality Nobody's Talking About

ai agents are having a moment right now. every startup pitch deck has them. every conference talk mentions them. everyone's building them

but here's what nobody's saying out loud: most of these agents are trash in production

they work great in the demo. they nail the happy path. then you put them in front of real users with real data and real edge cases, and suddenly you're debugging why your agent decided to delete customer data because it "thought that's what you wanted"

the demo vs production gap

building an agent that works in a controlled environment is trivial now. seriously, you can spin up a LangChain agent in an afternoon that'll impress your PM

but production is where agents go to die

here's what breaks when you actually ship:

• reliability collapses: your agent works 95% of the time in testing. in production with diverse user inputs? suddenly it's 70%. and 70% reliability is 0% useful
• costs explode: that innocent-looking agent that makes 5 LLM calls per request? congratulations, you just 10x'd your API bill
• latency becomes embarrassing: users expect responses in seconds. your agent is taking 30 seconds because it's "thinking deeply" (read: retrying failed tool calls)
• observability is a nightmare: something went wrong. good luck figuring out which of the 12 LLM calls in the agent's reasoning chain caused the failure

the three things that actually matter

after watching a bunch of teams (including my own) struggle with agents in production, there are really only three things that matter:

1. Evaluation Infrastructure

you can't improve what you can't measure. and with agents, measurement is stupidly hard

traditional software: did the function return the right output? yes/no, done

agents: did the agent accomplish the user's intent? maybe? it used the right tools but in the wrong order? it gave a correct answer but with terrible reasoning? it solved the problem but cost $10 in API calls?

you need:

• task-specific eval sets that actually represent production
• automated evaluation that doesn't just check exact matches
• continuous eval running on every prompt/agent change
• cost and latency metrics alongside quality metrics

without this, you're flying blind. every change is a gamble

2. Optimization Loop

your first version of an agent will suck. that's fine. what's not fine is having no systematic way to make it better

most teams are stuck in the "prompt engineering via vibes" phase. someone has a bad result, they tweak the prompt, hope it's better, ship it, repeat

that doesn't scale

you need:

• automated prompt optimization (like the GEPA stuff from the previous blog)
• A/B testing infrastructure for agents
• feedback loops from production failures back to eval
• version control and rollback for prompts and agent configs

basically: treat your agent like production software, not like a science experiment

3. Guardrails That Actually Work

agents are powerful because they can take actions autonomously. agents are dangerous because they can take actions autonomously

you need guardrails that prevent catastrophic failures without completely neutering the agent's capabilities

• input validation that catches malicious or nonsense requests
• output filtering that prevents hallucinated or harmful responses
• action approval for high-stakes operations
• rate limiting and cost caps per user/session
• circuit breakers that stop runaway agent loops

the information extraction use case

one area where agents are actually proving useful: information extraction from unstructured documents

this is the "take this 100-page contract and extract all the key terms into a structured format" problem

why it works:

• clear success criteria: did you extract the right fields accurately? yes/no, measurable
• tolerable failure mode: worst case, human reviews the output. not ideal, but not catastrophic
• high value per task: enterprises pay real money because manual extraction is painful
• bounded scope: the agent doesn't need to do anything creative, just accurate extraction

but even here, the quality bar is high. documents are long, schemas are complex, domain jargon is everywhere, and operational tolerance for errors is low

this is exactly where automated optimization shines. you can't manually tune prompts for every document type and schema variation. you need systematic optimization that improves accuracy without constant human intervention

what good production agents look like

the teams that are actually succeeding with agents in production have a few things in common:

• narrow scope: they're not building "do anything" agents. they're building "do this specific thing really well" agents
• human-in-the-loop where it matters: for high-stakes actions, the agent proposes, human approves
• continuous monitoring: they track quality, cost, and latency metrics in real-time
• fast iteration cycles: they can test and deploy agent improvements in hours, not weeks
• fallback strategies: when the agent fails, there's a graceful degradation path

it's not sexy. it's boring production engineering applied to a new primitive

but that's what actually works

the cost problem nobody wants to admit

let's talk about the elephant in the room: most production agents are way too expensive to be viable long-term

you build an agent that makes 10 LLM calls per request. each call costs $0.01. that's $0.10 per request. sounds fine until you're serving 10M requests/month. now you're spending $1M/month on LLM costs alone

and that's with a "cheap" model. if you're using Claude Opus or GPT-4 because "we need the best," multiply that by 10x

this is why prompt optimization and model selection matter so much. if you can:

• reduce the number of LLM calls per request
• use cheaper models with optimized prompts instead of expensive models with basic prompts
• cache results aggressively
• batch requests intelligently

you can cut costs by 10-100x without sacrificing quality

that's the difference between "this is too expensive to scale" and "this is actually profitable"

the observability nightmare

debugging agents in production is pain

traditional software: stack trace, error log, reproduce locally, fix bug, ship

agents: user reports "the agent gave me a weird response." ok cool, now you need to:

• reconstruct the entire chain of LLM calls
• figure out which tool calls succeeded/failed
• understand the agent's "reasoning" at each step
• identify if it was a prompt issue, model issue, tool issue, or data issue
• reproduce it (good luck if it was a rare edge case)

without good observability infrastructure, you're toast

you need:

• full trace logging of every agent execution
• ability to replay agent runs deterministically
• LLM response caching for faster iteration
• structured logging that captures intermediate states
• dashboards that show success rates, latencies, costs per agent/tool

this sounds like overkill until the first time a production agent fails mysteriously and you have zero visibility into why

what's actually working in production

not everything is doom and gloom. there are agent deployments that are genuinely working:

• customer support triage: agent reads ticket, categorizes, suggests responses. human reviews/edits before sending
• document processing: extract structured data from PDFs, contracts, forms. high accuracy on narrow domains
• code review assistants: flag potential issues, suggest improvements. developer has final say
• data analysis: answer questions about databases by writing/executing SQL. user validates results

pattern: agents that augment humans, not replace them. agents with clear tasks, measurable outputs, and human oversight for critical decisions

closing thoughts

ai agents aren't magic. they're software that happens to use LLMs for decision-making

treat them like production software:

• test rigorously
• monitor continuously
• optimize systematically
• fail gracefully
• iterate quickly

the teams that are succeeding aren't doing anything revolutionary. they're just applying good engineering practices to a new primitive

the teams that are struggling are treating agents like magic that "just works" without investing in the infrastructure to make them reliable, cost-effective, and observable

References

• Databricks - Building Production-Grade AI Agents - Real-world case study on deploying agents for information extraction with automated optimization and evaluation pipelines
• Anthropic - Claude Computer Use Research - Explores the challenges and safety considerations of autonomous AI agents performing real-world tasks
• LangSmith - Production observability and evaluation platform for LLM applications and agents. Shows real-world patterns for debugging and monitoring
• Confident AI - LLM Evaluation Metrics - Comprehensive guide on measuring agent quality beyond simple accuracy, including cost and latency considerations
• AgentBench Paper (2023) - Academic benchmark showing that even state-of-the-art agents fail on diverse real-world tasks, validating the production challenges discussed